tbuckley/cabot-search huggingface dataset:
Abstract 478: A novel dual-acting RAS/β-catenin inhibitor activates antitumor immunity to potentiate cancer immunotherapy
Authors: Md Yeashin Gazi, Ogacheko Okoko, Xin Wang, Caitlin Brandle, Zhi-chun Ding, Amit Mitra, Veronica Ramirez-Alcantara, Xi Chen, Adam Keeton, Yulia Maxuitenko, Gary Piazza, Gang Zhou
Journal: Cancer Research (2023)

Abstract Previous research suggests that the antineoplastic activity of sulindac is mediated by a cyclooxygenase (COX) independent mechanism involving dual inhibition of RAS and β-catenin signaling. ADT030 is a novel non-COX inhibitory sulindac-derivative designed to target RAS and phosphodiesterase 10 (PDE10) to block MAPK/AKT signaling and activate cGMP/PKG signaling to suppress oncogenic β-catenin transcription, respectively. ADT-030 potently and selectively inhibits growth and induces apoptosis of a variety of cancer cell lines and has robust antitumor activity in multiple mouse tumor models by oral administration. However, the impact of ADT-030 on the tumor microenvironment (TME) has not been examined. Here we report the antitumor activity and immunomodulatory effect of ADT-030 in preclinical models. We show that oral administration of ADT030 significantly inhibited the growth of multiple types of murine tumors in immunocompetent mice. In a metastatic breast tumor model (4T1), ADT-030 not only delayed the growth of the primary tumor but also reduced tumor metastasis, leading to improved mouse survival. Immunological analysis revealed marked reduction of MDSCs in mice, and increased T cell tumor infiltration after ADT-030 treatment. The antitumor activity of ADT030 was diminished in immunodeficient mice or immunocompetent mice depleted of CD8+ T cells, suggesting the dependence of ADT-030 on host immunity. Moreover, the combination of ADT-030 and anti-PD-1 antibody therapy led to further improved tumor growth control and mouse survival. Bulk and single-cell RNAseq is being conducted to determine the impact of ADT-030 on the cell compositions and transcriptomic changes in the TME. Altogether, our study provides insight into the mechanism of action of ADT-030 and underscores the potential of ADT-030 as a monotherapy or in combination with immunotherapies for RAS and Wnt/β-catenin driven cancers. Citation Format: Md Yeashin Gazi, Ogacheko Okoko, Xin Wang, Caitlin Brandle, Zhi-chun Ding, Amit Mitra, Veronica Ramirez-Alcantara, Xi Chen, Adam Keeton, Yulia Maxuitenko, Gary Piazza, Gang Zhou. A novel dual-acting RAS/β-catenin inhibitor activates antitumor immunity to potentiate cancer immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 478.
Abstract 5427: Improving non-small cell lung cancer segmentation on a challenging dataset
Authors: Yi Wei, Balaji Selvaraj, Mayank Patwari, Qin Li, Meng Xu, Konstantinos Sidiropoulos, Zhenning Zhang, Leon Fedden, Anant Madabhushi, Mohammadhadi Khorrami, Vidya Sankar Viswanathan, Amit Gupta
Journal: Cancer Research (2023)

Abstract When applied to different datasets, performance of the same deep learning tumor segmentation model can greatly vary. In a non-small cell lung cancer CT scan segmentation study that consists of two datasets, we found that the SwinUNETR model achieves state-of-the-art DICE score on a public dataset NSCLC but performs badly on a private dataset of curated data collected clinically. This performance variation reduces the applicability of such models. To mitigate this gap, through experimentation, we identified a set of techniques and applied them in the following order: (1) normalize a dataset to reduce differences between images. (2) stratify a dataset according to tumor sizes to form a more diverse training set. (3) isolate the lung area before training to help the model focus on the right area. (4) before training, initialize models with self-supervised pre-training weights (5) use a new loss function to give more weights on the cancerous area (6) after a model is trained, perform 3-axis test time flipping augmentation and ensemble the final predictions. In our experiments, our set of techniques improved the test DICE score for both datasets we tested on, where the best improvement was a 53% improvement from 0.32 to 0.49 DICE score. Citation Format: Yi Wei, Balaji Selvaraj, Mayank Patwari, Qin Li, Meng Xu, Konstantinos Sidiropoulos, Zhenning Zhang, Leon Fedden, Anant Madabhushi, Mohammadhadi Khorrami, Vidya Sankar Viswanathan, Amit Gupta. Improving non-small cell lung cancer segmentation on a challenging dataset. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5427.
Abstract 544: Dissecting the functional impairment of wild-type Tp53 in human glioblastoma
Authors: Leopoldo A. García-Montaño, Yamhilette Licón Muñoz, Frank Martinez, Kandee Gallegos, Huining Kang, Sara G. Piccirillo
Journal: Cancer Research (2023)

Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults and is characterized by heterogeneous nature, invasive potential, and poor treatment response. Work from The Cancer Genome Atlas (TCGA) has identified the tumor suppressor gene TP53 as one of the GBM drivers. However, in contrast to several cancers, only ~ 35% of GBM cases carry mutations in the TP53 gene. Furthermore, recent work from our lab showed that TP53 mutations emerge as ‘late’ events during GBM growth. This led us to hypothesize that wild-type (wt) Tp53 function is impaired in GBM. To test our hypothesis, we used clinical and molecular data of 591 GBM patients from TCGA and compared survival, age, gender, race, TP53 levels, and MGMT methylation status between wt and mutant patients. We also compiled a list of key players regulating Tp53 activity and used expression data to (i) compare the correlation of TP53 levels with the expression of each player and (ii) identify differentially expressed genes between our two groups. Our preliminary data suggest that wt TP53 patients live less in comparison to mutant patients and that both groups respond similarly to treatment, suggesting impairment of wt Tp53 function. We did not observe differences in age, sex, race, TP53 levels, or MGMT methylation status between the two groups. However, correlation analysis revealed that, among Tp53 activators, MAPK8 shows a strong negative correlation with TP53 expression in the wt group, raising the possibility that Tp53 function may be impaired. Furthermore, gene expression analysis of key Tp53 players showed that approximately 21% are differentially expressed between wt and mutant patients. Altogether, our results suggest that Tp53 function is impaired in wt patients and provide insights into the possible players affecting its activity. Our future work will focus on validating these results using GBM patient-derived cells and mouse models. Citation Format: Leopoldo A. García-Montaño, Yamhilette Licón Muñoz, Frank Martinez, Kandee Gallegos, Huining Kang, Sara G. Piccirillo. Dissecting the functional impairment of wild-type Tp53 in human glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 544.
Abstract 542: Synthetic lethal targeting of AURKA in head and neck squamous cell carcinoma
Authors: Flaviane N da Silva, Theodore Nguyen, Jong Woo Lee, Barbara Burtness, Erica Golemis
Journal: Cancer Research (2023)

Abstract The purpose of this study was to identify new combination strategies for improving treatment of head and neck squamous cell carcinomas (HNSCCs). HNSCCs often express high levels of Aurora kinase A (AURKA), most studied as a kinase important for mitotic progression in normal cells. Notably the overexpression of AURKA in many cancers allows it to act throughout the cell cycle, acquiring new cancer-promoting activities. While several AURKA inhibitors are in clinical trials, limited efficacy is observed with single treatment, causing us to evaluate potential AURKA combination therapeutic combinations. Particularly relevant to HNSCC, which often overexpress and depend on EGFR, overexpression of AURKA can promote resistance to EGFR inhibitors by mechanisms variously identified as including activation of the EGFR effectors AKT, ERK, and BIM. Using viability and clonogenic assays, we have found that the AURKA inhibitor VIC-1911 synergizes with the EGFR inhibitors afatinib and erlotinib in multiple HNSCC cell lines and retains activity in HNSCC cell models selected for resistance to these EGFR inhibitors. However, AURKA inhibition increases levels of AURKA protein and its protein-stabilizing partner TPX2, counteracting drug effect. We also combined inhibition of AURKA with inhibition of WEE1, a regulator of the G2/M checkpoint, finding this dual inhibition enhanced levels of apoptosis in EGFR inhibitor resistant HNSCC cell lines. Intriguingly, one recent study reported AURKA can also modulate the glycolytic signaling pathway, increasing the cancer-promoting Warburg effect. We have found that inhibition of AURKA can significantly reduce glycolytic flux in HNSCC cell lines and alter expression and phosphorylation of key glycolytic enzymes. Dual inhibition of AURKA in combination with inhibitors of glycolytic enzymes, or enzymes regulating oxidative phosphorylation, showed marked synergy. These and other results investigating AURKA signaling mechanisms in HNSCC demonstrate that the non-canonical functions of AURKA include viable therapeutic targets that can enhance the efficacy of AURKA inhibition in HNSCC. Citation Format: Flaviane N da Silva, Theodore Nguyen, Jong Woo Lee, Barbara Burtness, Erica Golemis. Synthetic lethal targeting of AURKA in head and neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 542.
Abstract 5420: Deep learning-based mapping of tertiary lymphoid structure scores from H&amp;E images of renal cell carcinoma trained by spatial transcriptomics data
Authors: Seungho Cook, Haenara Shin, Mi-Kyoung Seo, Dae Seung Lee, Hongyoon Choi
Journal: Cancer Research (2023)

Abstract Purpose Tertiary lymphoid structures are organized aggregates of immune cells present in the tumor microenvironment (TME) in which novel targets as well as beneficial biomarkers for immunotherapy in cancer were found. Here, we have developed and validated a deep learning model by integrating H&amp;E images of renal cell carcinoma and spatial transcriptomics data to infer spatial mapping of tertiary lymphoid structure scores in TME only using hematoxylin and eosin (H&amp;E) images. Methods A total of 20 H&amp;E images combined with spatial transcriptomics data of renal cell carcinoma were used to develop a model. Tertiary lymphoid structure scores can be acquired for each spot in spatial transcriptomics by geometric mean of the specific gene expression relevant to B cell, T cell, immunoglobulin, fibroblast, complement, and others. A convolutional neural network using H&amp;E image patches as inputs was developed to predict the tertiary lymphoid structure scores from H&amp;E-stained tissue image patches of renal cell carcinoma acquired by different patients. For the external validation, the model estimated the tertiary lymphoid structure scores from H&amp;E-stained tissue image patches of renal cell carcinoma of The Cancer Genome Atlas (TCGA-RCC). Results The tertiary lymphoid structure scores inferred by the model using H&amp;E image patches were significantly correlated with those derived by spatial transcriptomics data as an internal validation (r = 0.68, p &amp;lt; 1e-10). The mean value of the deep learning-based tertiary lymphoid structure scores estimated by the TCGA-RCC tissue images was significantly correlated with the tertiary lymphoid structure scores, T cell enrichment scores and immune cell enrichment scores estimated by bulk RNA-seq data from the corresponding TCGA data. Conclusions A deep learning model to infer spatial tertiary lymphoid structure in the tumor microenvironment using H&amp;E images was developed. As the tertiary lymphoid structure is a key to predict responsiveness of immune checkpoint inhibitors, mapping the score only using H&amp;E images could be clinically translated into image-based biomarkers. This approach can provide objective and flexible deep learning-based models for characterizing tumor microenvironment related to spatial immune distribution. Citation Format: Seungho Cook, Haenara Shin, Mi-Kyoung Seo, Dae Seung Lee, Hongyoon Choi. Deep learning-based mapping of tertiary lymphoid structure scores from H&amp;E images of renal cell carcinoma trained by spatial transcriptomics data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5420.
Abstract 5424: A deep learning approach (AI) which accurately identifies breast tumor cells, tumor infiltrating lymphocytes (TILS) and fibroblasts from H&amp;E slides
Authors: Luiz Augusto Zillmann da Silva, Alistair R. Williams, Aidan Kubeyev, Andrea Giorni, Jordan Laurie, Prabu Sivasubramaniam, Matthew Foster, Matthew Griffiths, Uzma Asghar
Journal: Cancer Research (2023)

Abstract Background: Histopathology assessments of cancers require highly skilled pathologists, are labor intensive and prone to errors without proper training or fatigue. Machine learning can assist pathologists by increasing efficiency and minimizing individual variability. This study adapted a deep learning model to reliably identify 3 cell types from triple negative breast cancers fixed on H&amp;E slides and verified performance with an expert pathologist. Methods: We apply the U-Net architecture to analyse pathology slides fixed with triple negative breast cancer (TNBC) tissue. The public dataset NuCLS was used for training. Semantic segmentation was used to identify single cells of 3 types: tumor cells, fibroblasts, and tumor infiltrating lymphocyte (TILs). For validation, the pathologist annotated 8 random H&amp;E tiles. The 3 cell types accuracy for NuCLS and the model was evaluated by our pathologist. Results: Overall, there was a 73% agreement with the pathologists (Pathologist vs. NuCLS). A set of 1,555 (90%) TNBC slides were used for training and 173 for validation (10%; unseen data). Table 1 outlines the accuracy metrics for each cell type and for each comparison. Compared to our pathologist, the model accurately identified TILs (62%), followed by fibroblasts (42%) and lastly tumor cells (26%). A significant source of discrepancy was variation in labeled single cell boundaries. The model was better at identifying TILs. The pathologist took 1.5 hours to annotate 8 tiles for the 3 cells and our model 644ms. Conclusion: It is possible to develop a deep learning model to identify breast cancer cells, fibroblasts and TILs from H&amp;E stained slides, with similar accuracy levels as a trained pathologist. The model performed better than a pathologist in identifying TILs, but both struggled with fibroblasts. Accuracy of 71% overall and 87% for TILs, motivates expansion to further datasets and other cancer types. Table 1. - Accuracy metrics. Quality assessment8 H&amp;E tiles Pathologist vs. NuCLS U-Net model vs. NuCLs U-Net model vs. Pathologist U-Net model vs. NuCLs (Full) Background 86% 71% 62% 73% Tumour 43% 35% 26% 71% Fibroblast 45% 39% 42% 31% TILs 34% 96% 62% 87% Overall 73% 74% 61% 71% Citation Format: Luiz Augusto Zillmann da Silva, Alistair R. Williams, Aidan Kubeyev, Andrea Giorni, Jordan Laurie, Prabu Sivasubramaniam, Matthew Foster, Matthew Griffiths, Uzma Asghar. A deep learning approach (AI) which accurately identifies breast tumor cells, tumor infiltrating lymphocytes (TILS) and fibroblasts from H&amp;E slides. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5424.
Abstract 4812: Inhibition of Smurf2 E3 ubiquitin ligase by heclin and its analogues enhances HIF-1α expression and transcriptional activity in normoxia or hypoxia
Authors: Shuai Zhao, Wafik S. El-Deiry
Journal: Cancer Research (2023)

Abstract Hypoxia-inducible factors (HIFs) act as transcription factors and play an essential role in cellular and systemic responses to low oxygen environments. HIF-1α expression is induced in acute hypoxia typically through failure of its ubiquitination and degradation mediated by Von Hippel-Lindau (VHL). Previously we discovered a non-canonical mechanism where the SMAD specific E3 ubiquitin protein ligase 2 (Smurf2) promotes the ubiquitination of HIF-1α and reduces HIF-1α level in HCT116 colorectal cancer cells. Smurf2 is a HECT-type ubiquitin ligase known to interact with Smad proteins, leading to their ubiquitination and proteasomal degradation. Overexpression of Smurf2 decreased HIF-1α expression in HCT116 and SW480 colorectal cancer cells under hypoxia as well as in RCC4 VHL-deficient kidney renal clear cell carcinoma cells under normoxia. Treatment with MG132 at least partially rescued the expression of HIF-1α following Smurf2 overexpression, indicating involvement of proteasome-dependent degradation in Smurf2-mediated HIF-1α destabilization. Knockdown of SMURF2 increased HIF-1α expression under normoxia in HCT116 and RCC4 cells. To investigate the effect of Smurf2 inhibition, we tested a selective reversible inhibitor of HECT E3 ubiquitin ligases, heclin, along with its analogues, PYR-41, C646 and 4E1RCat. Treatment with heclin increased HIF-1α expression in HCT116 under hypoxia as well as in SW480 and RCC4 cells under normoxia in a dose-dependent manner. PYR-41 elevated the level of HIF-1α level in HCT116 cells under hypoxia, in RCC4 cells under normoxia and in SW480 cells under both normoxia and hypoxia. To examine the effect on HIF transcriptional activity, we performed luciferase reporter assay using plasmids containing the hypoxia response element (HRE) from the PGK1 promoter and the VEGF promoter, respectively. PYR-41 induced transcriptional activity on PGK1-HRE in both HCT116 and SW480 cells. 4E1RCat robustly activated transcription on both VEGF-HRE and PGK1-HRE in HCT116 and SW480 cells. In summary, Smurf2 targets HIF-1α for ubiquitination and degradation independently of oxygen concentration and inhibition of Smurf2 to stimulate HIF activity under normoxia or hypoxia may be beneficial in pathological circumstances featuring anemia and hypoxemia. Citation Format: Shuai Zhao, Wafik S. El-Deiry. Inhibition of Smurf2 E3 ubiquitin ligase by heclin and its analogues enhances HIF-1α expression and transcriptional activity in normoxia or hypoxia. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4812.
Abstract 2541: Heterogeneity of VDAC2-Bak/Bax mediated mitochondrial apoptosis can be exploited to develop effective and selective treatment against metastatic uveal melanoma
Authors: Piyush Mishra
Journal: Cancer Research (2023)

Abstract Apoptosis, a form of programmed cell death, is an evolutionarily conserved and genetically regulated phenomenon that is pivotal in maintaining tissue homeostasis and is an important player in regulating carcinogenesis. Mitochondria along with its resident proteins are the central player in regulating apoptosis. In recent years, VDAC2, an Outer Mitochondrial Membrane (OMM) protein has been shown to be the key recruitment factor for Bak, a pro-apoptotic protein to the OMM and, therefore is crucial for tBid-induced OMM permeabilization. We have previously found that unexpectedly, hepatocellular carcinoma (HCC) cells are vastly more sensitive to tBid induced apoptosis compared to normal hepatocytes and this difference is owed to high abundance of VDAC2-Bak in HCC. These findings are in line with multiple datasets posted in Oncomine, and protein atlas websites and may have clinical relevance. Combination of S63845, a selective Mcl-1 inhibitor, with TRAIL was effective at reducing tumor growth in vivo, but only in tumors expressing VDAC2. We hypothesized that the liver metastases of some extrahepatic primary tumors might also show enhanced sensitivity to VDAC2-Bak mediated apoptosis as compared to the normal liver tissue. Testing this hypothesis is important for uveal melanoma (UM) that primarily metastasizes in the liver, and these metastases lack an effective treatment. Interestingly, on testing several UM cell lines and patient samples we observed that VDAC2-Bak are upregulated in UM liver metastasis as well as compared to normal liver tissue. As expected, high VDAC2 expressing cell lines were also more sensitive to tBid induced OMM permeabilization. We propose that these differences can be exploited to design an effective therapy for metastasized UM. Citation Format: Piyush Mishra. Heterogeneity of VDAC2-Bak/Bax mediated mitochondrial apoptosis can be exploited to develop effective and selective treatment against metastatic uveal melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2541.